Realistic History Matching of Cyclic Steam Stimulation Performance of Several Groups of Multilateral Wells in the Peace River Field, Canada
Bibliographic record
Abstract
Abstract With 8 billion barrels of bitumen in place and more than 30 years of thermal piloting and demonstration projects, Peace River offers an excellent growth opportunity for Shell's ultraheavy oil portfolio. In support of this initiative, integrated geological and reservoir modeling of two project areas was conducted. The key objectives were to improve predictive modeling capability of cyclic steam stimulation (CSS) projects by history matching two groups of CSS multilateral wells and develop a history matched physical representation that not only validates empirical models but can be deployed to optimize CSS designs for full field development. Detailed geological models were created over two pad areas providing a geological framework large enough to have realistic boundary conditions, including impact of surrounding wells. The geological models were imported into CMG's STARS thermal reservoir simulator, and a relatively fine grid was extended over each project area. All available historical production, injection, pressure and temperature data were used in history matching. Steam-induced reservoir dilation, explicit fracturing, and relative permeability hysteresis were important aspects of the overall physical representation. Common physical parameters for dilation/re-compaction, fractures, permeability/porosity transforms, vertical to horizontal permeability ratios, and relative permeability hysteresis were used for both pads. Each pad area maintained its own unique geological, petrophysical, and fluid properties, in line with observed field trends. Excellent history matches (aided by experimental design) of injection and production volumes, injection wellhead pressures, estimated production bottom-hole pressures and temperature profiles were achieved not only for the entire Pad A and B groups of wells, but also for the individual wells. In summary, a predictive CSS simulation model has been developed and validated by history matching two areas of the Peace River field. The model is suitable for sensitivity studies of geological, petrophysical, and fluid properties. It is also capable of assessing impact of well configuration, spacing, steam quality, and steaming strategy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".